"""simple docstring"""



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
   import tensorflow as tf

   from ..tf_utils import stable_softmax


if is_torch_available():
   import torch


SCREAMING_SNAKE_CASE__:Any									=			logging.get_logger(__name__)






@add_end_docstrings(
    snake_case_,						r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """,						)
class  snake_case__      (       snake_case_       ):


 def 						a__   ( self    ,		lowerCamelCase						):
        if self.framework == "tf":
               __a       =    tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()
        elif self.framework == "pt":
               __a       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,		as_tuple=lowerCamelCase						)
        else:
               raise ValueError("Unsupported framework"						)
        return masked_index


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.get_masked_index(lowerCamelCase						)
        __a       =    np.prod(masked_index.shape						)
        if numel < 1:
               raise PipelineException(
                   "fill-mask"    ,		self.model.base_model_prefix    ,		F"No mask_token ({self.tokenizer.mask_token}) found on the input"    ,		)


 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               for model_input in model_inputs:
                      self._ensure_exactly_one_mask_token(model_input["input_ids"][0]						)
        else:
               for input_ids in model_inputs["input_ids"]:
                      self._ensure_exactly_one_mask_token(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
        if return_tensors is None:
               __a       =    self.framework
        __a       =    self.tokenizer(lowerCamelCase    ,		return_tensors=lowerCamelCase						)
        self.ensure_exactly_one_mask_token(lowerCamelCase						)
        return model_inputs


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    self.model(**lowerCamelCase						)
        __a       =    model_inputs["input_ids"]
        return model_outputs


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=5    ,		lowerCamelCase=None						):
        # Cap top_k if there are targets
        if target_ids is not None and target_ids.shape[0] < top_k:
               __a       =    target_ids.shape[0]
        __a       =    model_outputs["input_ids"][0]
        __a       =    model_outputs["logits"]

        if self.framework == "tf":
               __a       =    tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()[:, 0]

               __a       =    outputs.numpy()

               __a       =    outputs[0, masked_index, :]
               __a       =    stable_softmax(lowerCamelCase    ,		axis=-1						)
               if target_ids is not None:
                      __a       =    tf.gather_nd(tf.squeeze(lowerCamelCase    ,		0						)    ,		target_ids.reshape(-1    ,		1						)						)
                      __a       =    tf.expand_dims(lowerCamelCase    ,		0						)

               __a       =    tf.math.top_k(lowerCamelCase    ,		k=lowerCamelCase						)
               __a ,   __a       =    topk.values.numpy(), topk.indices.numpy()
        else:
               __a       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,		as_tuple=lowerCamelCase						).squeeze(-1						)
               # Fill mask pipeline supports only one ${mask_token} per sample

               __a       =    outputs[0, masked_index, :]
               __a       =    logits.softmax(dim=-1						)
               if target_ids is not None:
                      __a       =    probs[..., target_ids]

               __a ,   __a       =    probs.topk(lowerCamelCase						)

        __a       =    []
        __a       =    values.shape[0] == 1
        for i, (_values, _predictions) in enumerate(zip(values.tolist()    ,		predictions.tolist()						)						):
               __a       =    []
               for v, p in zip(_values    ,		_predictions						):
                      # Copy is important since we're going to modify this array in place
                      __a       =    input_ids.numpy().copy()
                      if target_ids is not None:
                             __a       =    target_ids[p].tolist()

                      __a       =    p
                      # Filter padding out:
                      __a       =    tokens[np.where(tokens != self.tokenizer.pad_token_id						)]
                      # Originally we skip special tokens to give readable output.
                      # For multi masks though, the other [MASK] would be removed otherwise
                      # making the output look odd, so we add them back
                      __a       =    self.tokenizer.decode(lowerCamelCase    ,		skip_special_tokens=lowerCamelCase						)
                      __a       =    {"score": v, "token": p, "token_str": self.tokenizer.decode([p]						), "sequence": sequence}
                      row.append(lowerCamelCase						)
               result.append(lowerCamelCase						)
        if single_mask:
               return result[0]
        return result


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               __a       =    [targets]
        try:
               __a       =    self.tokenizer.get_vocab()
        except Exception:
               __a       =    {}
        __a       =    []
        for target in targets:
               __a       =    vocab.get(lowerCamelCase    ,		lowerCamelCase						)
               if id_ is None:
                      __a       =    self.tokenizer(
                          lowerCamelCase    ,		add_special_tokens=lowerCamelCase    ,		return_attention_mask=lowerCamelCase    ,		return_token_type_ids=lowerCamelCase    ,		max_length=1    ,		truncation=lowerCamelCase    ,		)["input_ids"]
                      if len(lowerCamelCase						) == 0:
                             logger.warning(
                                 F"The specified target token `{target}` does not exist in the model vocabulary. "
                                 "We cannot replace it with anything meaningful, ignoring it"						)
                             continue
                      __a       =    input_ids[0]
                      # XXX: If users encounter this pass
                      # it becomes pretty slow, so let's make sure
                      # The warning enables them to fix the input to
                      # get faster performance.
                      logger.warning(
                          F"The specified target token `{target}` does not exist in the model vocabulary. "
                          F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_						)}`."						)
               target_ids.append(id_						)
        __a       =    list(set(lowerCamelCase						)						)
        if len(lowerCamelCase						) == 0:
               raise ValueError("At least one target must be provided when passed."						)
        __a       =    np.array(lowerCamelCase						)
        return target_ids


 def 						a__   ( self    ,		lowerCamelCase=None    ,		lowerCamelCase=None						):
        __a       =    {}

        if targets is not None:
               __a       =    self.get_target_ids(lowerCamelCase    ,		lowerCamelCase						)
               __a       =    target_ids

        if top_k is not None:
               __a       =    top_k

        if self.tokenizer.mask_token_id is None:
               raise PipelineException(
                   "fill-mask"    ,		self.model.base_model_prefix    ,		"The tokenizer does not define a `mask_token`."						)
        return {}, {}, postprocess_params






 def __call__( self    ,		lowerCamelCase    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    super().__call__(lowerCamelCase    ,		**lowerCamelCase						)
        if isinstance(lowerCamelCase    ,		lowerCamelCase						) and len(lowerCamelCase						) == 1:
               return outputs[0]
        return outputs

"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
